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    \bfseries \zihao{3} 摘~要
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随着人工智能与机器人技术的进步，如何使仿人机器人表现出更加接近真人的动作成为了研究热点。本文以高擎Mini$\pi$双足机器人作为实验平台，基于深度强化学习算法，设计并构建了一套用于仿人机器人足球技能学习的课程与训练框架，实现了在模拟环境中对机器人行走与踢球策略的训练，并在实物上进行了策略部署。

本文首先在IsaacGym中搭建了一套适用于Mini$\pi$机器人的仿真环境。同时，针对行走与踢球任务，设计了两套奖励函数，对相关的参数进行了微调。并采用了域随机化与扰动机制，提高了策略的泛化能力和鲁棒性。

为了验证策略的跨平台迁移能力，本文设计了Sim2Sim流程，将训练出的策略迁移至Gazebo仿真平台，实现了稳定的行走和踢球。进一步，编写实物控制接口，将策略在实物平台上进行了部署验证，能够实现较为平稳的行走行为，验证了所提出方法的可行性与一定的迁移能力。

\textbf{关键词}：深度强化学习；仿人机器人；PPO算法；实物部署
  
    
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    \bfseries \zihao{3} Abstract
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With the rapid development of artificial intelligence and robotics, enabling humanoid robots to perform movements that closely resemble those of humans has become a prominent research focus. This paper takes the GaoQing Mini$\pi$ bipedal robot as the experimental platform and proposes a curriculum-based training framework for humanoid robot soccer skill acquisition using deep reinforcement learning. Walking and kicking strategies are trained in simulation and subsequently deployed to the physical robot.

A customized simulation environment for the Mini$\pi$ robot is first constructed in IsaacGym. Task-specific reward functions are designed for both walking and kicking behaviors, with careful parameter tuning. Domain randomization and perturbation techniques are employed to improve the generalization and robustness of the learned policies.

To evaluate cross-platform transferability, a Sim2Sim pipeline is developed to migrate the trained policies to the Gazebo simulation environment, where stable walking and kicking behaviors are successfully demonstrated. Furthermore, a physical control interface is implemented to deploy the learned policies onto the real robot, achieving smooth walking performance and validating the feasibility and transferability of the proposed approach.

Keywords: Deep Reinforcement Learning; Humanoid Robot; PPO Algorithm; Physical Deployment